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For the 4 1/2 years of my PhD, I worked with a de-identified dataset that felt like nothing more than numbers on a page. Cold. Abstract. Disconnected from any real human experience. Each “person” was just a line in an Excel spreadsheet, with an ID in place of a name. When I started my first role in insurance pricing, my mindset initially remained the same. That was until my boss took me along to speak to a policyholder - putting me face-to-face with one of the people my data actually represented. I sat across from a man who was running a business that was struggling to make ends meet - one of the few Australian manufacturing companies that was adamant their product would continue to remain Australian-made. The premium increase that I didn’t think twice about when performing the calculation was causing him genuine financial distress. As he told his story, I could see him blinking back tears at times. That day, everything changed for me. I realised my data was more than just numbers - it represented actual human beings, with emotions and struggles, who behave in sometimes unpredictable ways. This lesson has became even more relevant as I’ve watched our world become increasingly app-driven. Today, understanding the human behaviour behind every click, scroll, and purchase has become absolutely critical for business success. That’s exactly what we dive into in the latest episode of Value Driven Data Science, where I’m joined by Miguel Curiel, Product Analytics Manager at Bloomberg. In our conversation, Miguel breaks down:
Miguel is currently writing his own book on product analytics, so you’re getting insights from someone literally writing the playbook on this emerging field. If you’ve ever wondered how Netflix knows exactly what to recommend next, or how companies like Bloomberg optimize their digital products, this episode pulls back the curtain on the human psychology driving those decisions. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 84: The 7-Step Checklist for Creating Business Impact Through Product Analytics Talk again soon, Dr Genevieve Hayes |
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
Most data scientists think the hardest part of experimentation is the statistics. It’s not. It’s telling people their ideas didn’t work. Here’s a reality check about experimentation: Even at companies like Google and Netflix, 70-90% of experiments don’t show positive results. That means if you’re running A/B tests, you’ll be delivering “bad news” far more often than good news. Now imagine being the data scientist who constantly tells people their ideas didn’t work. How long before...
"The show doesn't go on because it's ready; it goes on because it's 11:30." - Lorne Michaels, creator of Saturday Night Live. Data scientists can learn a lot from Saturday Night Live. SNL has a rule: The show goes on at 11:30. Not when it’s perfect. Not when everyone’s happy with it. At 11:30. Many years ago, I was responsible for performing the annual workcover premium rate calculation for the whole of Victoria. It was a calculation of the utmost importance - $2b in revenue depended on it...
12 years in government taught me something surprising about data science. Making money and making an impact aren’t always the same thing. The easiest way to create value as a data scientist is to help your organisation to make more money. After all, everyone wants more money, don’t they? As Elon Musk’s recent $1 trillion pay deal suggests, even the richest person on Earth. Yet, while money is valuable, money and value aren’t necessarily the same thing. And if you work for a not-for-profit or...